This paper presents a stochastic subspace identification algorithm to compute stable, minimum phase models from a stationary time-series data. The algorithm is based on spectral factorization techniques and a stochastic subspace identification method via a block LQ decomposition (Tanaka and Katayama, 2003c). Two Riccati equations are solved to ensure both stability and minimum phase property of...

We present recent developments in two-stage mixed-integer stochastic programming with regard to application in power production planning. In particular, we review structural properties, stability issues, scenario reduction and decomposition algorithms for two-stage models. Furthermore, we describe an application to stochastic thermal unit commitment.

We propose a new approach to risk modeling in power optimization employing the concept of stochastic dominance. This leads to new classes of large-scale block-structured mixed-integer linear programs for which we present decomposition algorithms. The new methodology is applied to stochastic optimization problems related to operation and investment planning in a power system with dispersed gener...

Almost sure asymptotic stability of stochastic difference and differential equations with non-anticipating memory terms is studied in R. Sufficient criteria are obtained with help of Lyapunov-Krasovskǐi-type functionals, martingale decomposition and semi-martingale convergence theorems. The results allow numerical methods for stochastic differential equations with memory to be studied in terms ...

The contribution deals with a stochastic process which cumulates random increments at random moments, the cumulative process. A multiplicative model of rate of cumulation, with regression on covariate processes, is studied. We show the consistency of estimators and then the asymptotic normality of the process of residuals. An application dealing with the process of growing damage of a technical...

Under specific parametric assumptions, an n−variable structural vector auto-regression (SVAR) can be identified (up to n! shock orderings) via heteroskedasticity of the structural shocks (Rigobon, 2003, Sentana & Fiorentini, 2001). I show that misspecification of the heteroskedasticity process can bias results derived from these identification schemes. I propose a different method that identifi...

Motivated by the applications in routing in data centers, we study the problem of expressing an n × n doubly stochastic matrix as a linear combination using the smallest number of (sub)permutation matrices. The Birkhoff-von Neumann decomposition theorem proves that there exists such a decomposition, but does not give a representation with the smallest number of permutation matrices. In particul...

We present an extension of the Generalized Spectral Decomposition method for the resolution of non-linear stochastic problems. The method consists in the construction of a reduced basis approximation of the Galerkin solution and is independent of the stochastic discretization selected (polynomial chaos, stochastic multi-element or multiwavelets). Two algorithms are proposed for the sequential c...

We propose a novel approach to the decomposition of large probabilistic models. The goal of our method is to avoid the evaluation of the subnetworks obtained by decomposition for all values of the state description vector, as would be necessary with a standard aggregation and decomposition approach. Instead, we propose a fixed-point iteration that requires the evaluation of the subnetwork for o...